docstr: Args
  
      forward_func:  The forward function of the model or any modification of it
      layer: Layer for which output attributions are computed.
             Output size of attribute matches that of layer output.
docstr_tok: ["Args","forward_func",":","The","forward","function","of","the","model","or","any","modification","of","it","layer",":","Layer","for","which","output","attributions","are","computed",".","Output","size","of","attribute","matches","that","of","layer","output","."]
code: def __init__(self, forward_func, layer) -> None:
      r"""
      Args
  
          forward_func:  The forward function of the model or any modification of it
          layer: Layer for which output attributions are computed.
                 Output size of attribute matches that of layer output.
      """
      super().__init__(forward_func, layer)
  
file_path: captum/tests/attr/helpers/conductance_reference.py
imports: import numpy
  import torch
  from captum._utils.gradient import apply_gradient_requirements, undo_gradient_requirements
  from captum.attr._utils.approximation_methods import approximation_parameters
  from captum.attr._utils.attribution import LayerAttribution
  from captum.attr._utils.common import _reshape_and_sum
label_desc: Use image node_id 1 to create a new ConductanceReference object from inherited base classes: LayerAttribution with example: obj = ConductanceReference(forward_func, layer)